# coding=utf-8 # Copyright 2022 Microsoft Research and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch LayoutLMv3 model.""" import collections import math from typing import Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward from ...utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, torch_int, ) from .configuration_layoutlmv3 import LayoutLMv3Config logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "LayoutLMv3Config" LAYOUTLMV3_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`LayoutLMv3Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ LAYOUTLMV3_MODEL_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*): Bounding boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size, config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height / config.patch_size) * (width / config.patch_size))`. attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ LAYOUTLMV3_DOWNSTREAM_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*): Bounding boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size, config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height / config.patch_size) * (width / config.patch_size))`. attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class LayoutLMv3PatchEmbeddings(nn.Module): """LayoutLMv3 image (patch) embeddings. This class also automatically interpolates the position embeddings for varying image sizes.""" def __init__(self, config): super().__init__() image_size = ( config.input_size if isinstance(config.input_size, collections.abc.Iterable) else (config.input_size, config.input_size) ) patch_size = ( config.patch_size if isinstance(config.patch_size, collections.abc.Iterable) else (config.patch_size, config.patch_size) ) self.patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1]) self.proj = nn.Conv2d(config.num_channels, config.hidden_size, kernel_size=patch_size, stride=patch_size) def forward(self, pixel_values, position_embedding=None): embeddings = self.proj(pixel_values) if position_embedding is not None: # interpolate the position embedding to the corresponding size position_embedding = position_embedding.view(1, self.patch_shape[0], self.patch_shape[1], -1) position_embedding = position_embedding.permute(0, 3, 1, 2) patch_height, patch_width = embeddings.shape[2], embeddings.shape[3] position_embedding = F.interpolate(position_embedding, size=(patch_height, patch_width), mode="bicubic") embeddings = embeddings + position_embedding embeddings = embeddings.flatten(2).transpose(1, 2) return embeddings class LayoutLMv3TextEmbeddings(nn.Module): """ LayoutLMv3 text embeddings. Same as `RobertaEmbeddings` but with added spatial (layout) embeddings. """ def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False ) self.padding_idx = config.pad_token_id self.position_embeddings = nn.Embedding( config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx ) self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size) self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size) self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size) self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size) def calculate_spatial_position_embeddings(self, bbox): try: left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0]) upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1]) right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2]) lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3]) except IndexError as e: raise IndexError("The `bbox` coordinate values should be within 0-1000 range.") from e h_position_embeddings = self.h_position_embeddings(torch.clip(bbox[:, :, 3] - bbox[:, :, 1], 0, 1023)) w_position_embeddings = self.w_position_embeddings(torch.clip(bbox[:, :, 2] - bbox[:, :, 0], 0, 1023)) # below is the difference between LayoutLMEmbeddingsV2 (torch.cat) and LayoutLMEmbeddingsV1 (add) spatial_position_embeddings = torch.cat( [ left_position_embeddings, upper_position_embeddings, right_position_embeddings, lower_position_embeddings, h_position_embeddings, w_position_embeddings, ], dim=-1, ) return spatial_position_embeddings def create_position_ids_from_input_ids(self, input_ids, padding_idx): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask)) * mask return incremental_indices.long() + padding_idx def create_position_ids_from_inputs_embeds(self, inputs_embeds): """ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. """ input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape) def forward( self, input_ids=None, bbox=None, token_type_ids=None, position_ids=None, inputs_embeds=None, ): if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx).to( input_ids.device ) else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings spatial_position_embeddings = self.calculate_spatial_position_embeddings(bbox) embeddings = embeddings + spatial_position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class LayoutLMv3PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = LayoutLMv3Config base_model_prefix = "layoutlmv3" def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) elif isinstance(module, LayoutLMv3Model): if self.config.visual_embed: module.cls_token.data.zero_() module.pos_embed.data.zero_() class LayoutLMv3SelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.has_relative_attention_bias = config.has_relative_attention_bias self.has_spatial_attention_bias = config.has_spatial_attention_bias def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def cogview_attention(self, attention_scores, alpha=32): """ https://arxiv.org/abs/2105.13290 Section 2.4 Stabilization of training: Precision Bottleneck Relaxation (PB-Relax). A replacement of the original nn.Softmax(dim=-1)(attention_scores). Seems the new attention_probs will result in a slower speed and a little bias. Can use torch.allclose(standard_attention_probs, cogview_attention_probs, atol=1e-08) for comparison. The smaller atol (e.g., 1e-08), the better. """ scaled_attention_scores = attention_scores / alpha max_value = scaled_attention_scores.amax(dim=(-1)).unsqueeze(-1) new_attention_scores = (scaled_attention_scores - max_value) * alpha return nn.Softmax(dim=-1)(new_attention_scores) def forward( self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, rel_pos=None, rel_2d_pos=None, ): mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. # The attention scores QT K/√d could be significantly larger than input elements, and result in overflow. # Changing the computational order into QT(K/√d) alleviates the problem. (https://arxiv.org/pdf/2105.13290.pdf) attention_scores = torch.matmul(query_layer / math.sqrt(self.attention_head_size), key_layer.transpose(-1, -2)) if self.has_relative_attention_bias and self.has_spatial_attention_bias: attention_scores += (rel_pos + rel_2d_pos) / math.sqrt(self.attention_head_size) elif self.has_relative_attention_bias: attention_scores += rel_pos / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. # Use the trick of the CogView paper to stablize training attention_probs = self.cogview_attention(attention_scores) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs # Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput class LayoutLMv3SelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2Attention with LayoutLMv2->LayoutLMv3 class LayoutLMv3Attention(nn.Module): def __init__(self, config): super().__init__() self.self = LayoutLMv3SelfAttention(config) self.output = LayoutLMv3SelfOutput(config) def forward( self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, rel_pos=None, rel_2d_pos=None, ): self_outputs = self.self( hidden_states, attention_mask, head_mask, output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2Layer with LayoutLMv2->LayoutLMv3 class LayoutLMv3Layer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = LayoutLMv3Attention(config) self.intermediate = LayoutLMv3Intermediate(config) self.output = LayoutLMv3Output(config) def forward( self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, rel_pos=None, rel_2d_pos=None, ): self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class LayoutLMv3Encoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([LayoutLMv3Layer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False self.has_relative_attention_bias = config.has_relative_attention_bias self.has_spatial_attention_bias = config.has_spatial_attention_bias if self.has_relative_attention_bias: self.rel_pos_bins = config.rel_pos_bins self.max_rel_pos = config.max_rel_pos self.rel_pos_bias = nn.Linear(self.rel_pos_bins, config.num_attention_heads, bias=False) if self.has_spatial_attention_bias: self.max_rel_2d_pos = config.max_rel_2d_pos self.rel_2d_pos_bins = config.rel_2d_pos_bins self.rel_pos_x_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False) self.rel_pos_y_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False) def relative_position_bucket(self, relative_position, bidirectional=True, num_buckets=32, max_distance=128): ret = 0 if bidirectional: num_buckets //= 2 ret += (relative_position > 0).long() * num_buckets n = torch.abs(relative_position) else: n = torch.max(-relative_position, torch.zeros_like(relative_position)) # now n is in the range [0, inf) # half of the buckets are for exact increments in positions max_exact = num_buckets // 2 is_small = n < max_exact # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance val_if_large = max_exact + ( torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) ).to(torch.long) val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1)) ret += torch.where(is_small, n, val_if_large) return ret def _cal_1d_pos_emb(self, position_ids): rel_pos_mat = position_ids.unsqueeze(-2) - position_ids.unsqueeze(-1) rel_pos = self.relative_position_bucket( rel_pos_mat, num_buckets=self.rel_pos_bins, max_distance=self.max_rel_pos, ) # Since this is a simple indexing operation that is independent of the input, # no need to track gradients for this operation # # Without this no_grad context, training speed slows down significantly with torch.no_grad(): rel_pos = self.rel_pos_bias.weight.t()[rel_pos].permute(0, 3, 1, 2) rel_pos = rel_pos.contiguous() return rel_pos def _cal_2d_pos_emb(self, bbox): position_coord_x = bbox[:, :, 0] position_coord_y = bbox[:, :, 3] rel_pos_x_2d_mat = position_coord_x.unsqueeze(-2) - position_coord_x.unsqueeze(-1) rel_pos_y_2d_mat = position_coord_y.unsqueeze(-2) - position_coord_y.unsqueeze(-1) rel_pos_x = self.relative_position_bucket( rel_pos_x_2d_mat, num_buckets=self.rel_2d_pos_bins, max_distance=self.max_rel_2d_pos, ) rel_pos_y = self.relative_position_bucket( rel_pos_y_2d_mat, num_buckets=self.rel_2d_pos_bins, max_distance=self.max_rel_2d_pos, ) # Since this is a simple indexing operation that is independent of the input, # no need to track gradients for this operation # # Without this no_grad context, training speed slows down significantly with torch.no_grad(): rel_pos_x = self.rel_pos_x_bias.weight.t()[rel_pos_x].permute(0, 3, 1, 2) rel_pos_y = self.rel_pos_y_bias.weight.t()[rel_pos_y].permute(0, 3, 1, 2) rel_pos_x = rel_pos_x.contiguous() rel_pos_y = rel_pos_y.contiguous() rel_2d_pos = rel_pos_x + rel_pos_y return rel_2d_pos def forward( self, hidden_states, bbox=None, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True, position_ids=None, patch_height=None, patch_width=None, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None rel_pos = self._cal_1d_pos_emb(position_ids) if self.has_relative_attention_bias else None rel_2d_pos = self._cal_2d_pos_emb(bbox) if self.has_spatial_attention_bias else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, attention_mask, layer_head_mask, output_attentions, rel_pos, rel_2d_pos, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, all_hidden_states, all_self_attentions, ] if v is not None ) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaIntermediate class LayoutLMv3Intermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaOutput class LayoutLMv3Output(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states @add_start_docstrings( "The bare LayoutLMv3 Model transformer outputting raw hidden-states without any specific head on top.", LAYOUTLMV3_START_DOCSTRING, ) class LayoutLMv3Model(LayoutLMv3PreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config if config.text_embed: self.embeddings = LayoutLMv3TextEmbeddings(config) if config.visual_embed: # use the default pre-training parameters for fine-tuning (e.g., input_size) # when the input_size is larger in fine-tuning, we will interpolate the position embeddings in forward self.patch_embed = LayoutLMv3PatchEmbeddings(config) size = int(config.input_size / config.patch_size) self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.pos_embed = nn.Parameter(torch.zeros(1, size * size + 1, config.hidden_size)) self.pos_drop = nn.Dropout(p=0.0) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) if self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias: self.init_visual_bbox(image_size=(size, size)) self.norm = nn.LayerNorm(config.hidden_size, eps=1e-6) self.encoder = LayoutLMv3Encoder(config) self.init_weights() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) def init_visual_bbox(self, image_size=(14, 14), max_len=1000): """ Create the bounding boxes for the visual (patch) tokens. """ visual_bbox_x = torch.div( torch.arange(0, max_len * (image_size[1] + 1), max_len), image_size[1], rounding_mode="trunc" ) visual_bbox_y = torch.div( torch.arange(0, max_len * (image_size[0] + 1), max_len), image_size[0], rounding_mode="trunc" ) visual_bbox = torch.stack( [ visual_bbox_x[:-1].repeat(image_size[0], 1), visual_bbox_y[:-1].repeat(image_size[1], 1).transpose(0, 1), visual_bbox_x[1:].repeat(image_size[0], 1), visual_bbox_y[1:].repeat(image_size[1], 1).transpose(0, 1), ], dim=-1, ).view(-1, 4) cls_token_box = torch.tensor([[0 + 1, 0 + 1, max_len - 1, max_len - 1]]) self.visual_bbox = torch.cat([cls_token_box, visual_bbox], dim=0) def calculate_visual_bbox(self, device, dtype, batch_size): visual_bbox = self.visual_bbox.repeat(batch_size, 1, 1) visual_bbox = visual_bbox.to(device).type(dtype) return visual_bbox def forward_image(self, pixel_values): embeddings = self.patch_embed(pixel_values) # add [CLS] token batch_size, seq_len, _ = embeddings.size() cls_tokens = self.cls_token.expand(batch_size, -1, -1) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add position embeddings if self.pos_embed is not None: embeddings = embeddings + self.pos_embed embeddings = self.pos_drop(embeddings) embeddings = self.norm(embeddings) return embeddings @add_start_docstrings_to_model_forward( LAYOUTLMV3_MODEL_INPUTS_DOCSTRING.format("batch_size, token_sequence_length") ) @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, bbox: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Returns: Examples: ```python >>> from transformers import AutoProcessor, AutoModel >>> from datasets import load_dataset >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) >>> model = AutoModel.from_pretrained("microsoft/layoutlmv3-base") >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train", trust_remote_code=True) >>> example = dataset[0] >>> image = example["image"] >>> words = example["tokens"] >>> boxes = example["bboxes"] >>> encoding = processor(image, words, boxes=boxes, return_tensors="pt") >>> outputs = model(**encoding) >>> last_hidden_states = outputs.last_hidden_state ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None: input_shape = input_ids.size() batch_size, seq_length = input_shape device = input_ids.device elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] batch_size, seq_length = input_shape device = inputs_embeds.device elif pixel_values is not None: batch_size = len(pixel_values) device = pixel_values.device else: raise ValueError("You have to specify either input_ids or inputs_embeds or pixel_values") if input_ids is not None or inputs_embeds is not None: if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) if bbox is None: bbox = torch.zeros(tuple(list(input_shape) + [4]), dtype=torch.long, device=device) embedding_output = self.embeddings( input_ids=input_ids, bbox=bbox, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) final_bbox = final_position_ids = None patch_height = patch_width = None if pixel_values is not None: patch_height, patch_width = ( torch_int(pixel_values.shape[2] / self.config.patch_size), torch_int(pixel_values.shape[3] / self.config.patch_size), ) visual_embeddings = self.forward_image(pixel_values) visual_attention_mask = torch.ones( (batch_size, visual_embeddings.shape[1]), dtype=torch.long, device=device ) if attention_mask is not None: attention_mask = torch.cat([attention_mask, visual_attention_mask], dim=1) else: attention_mask = visual_attention_mask if self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias: if self.config.has_spatial_attention_bias: visual_bbox = self.calculate_visual_bbox(device, dtype=torch.long, batch_size=batch_size) if bbox is not None: final_bbox = torch.cat([bbox, visual_bbox], dim=1) else: final_bbox = visual_bbox visual_position_ids = torch.arange( 0, visual_embeddings.shape[1], dtype=torch.long, device=device ).repeat(batch_size, 1) if input_ids is not None or inputs_embeds is not None: position_ids = torch.arange(0, input_shape[1], device=device).unsqueeze(0) position_ids = position_ids.expand(input_shape) final_position_ids = torch.cat([position_ids, visual_position_ids], dim=1) else: final_position_ids = visual_position_ids if input_ids is not None or inputs_embeds is not None: embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1) else: embedding_output = visual_embeddings embedding_output = self.LayerNorm(embedding_output) embedding_output = self.dropout(embedding_output) elif self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias: if self.config.has_spatial_attention_bias: final_bbox = bbox if self.config.has_relative_attention_bias: position_ids = self.embeddings.position_ids[:, : input_shape[1]] position_ids = position_ids.expand_as(input_ids) final_position_ids = position_ids extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( attention_mask, None, device, dtype=embedding_output.dtype ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) encoder_outputs = self.encoder( embedding_output, bbox=final_bbox, position_ids=final_position_ids, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, patch_height=patch_height, patch_width=patch_width, ) sequence_output = encoder_outputs[0] if not return_dict: return (sequence_output,) + encoder_outputs[1:] return BaseModelOutput( last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class LayoutLMv3ClassificationHead(nn.Module): """ Head for sentence-level classification tasks. Reference: RobertaClassificationHead """ def __init__(self, config, pool_feature=False): super().__init__() self.pool_feature = pool_feature if pool_feature: self.dense = nn.Linear(config.hidden_size * 3, config.hidden_size) else: self.dense = nn.Linear(config.hidden_size, config.hidden_size) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) def forward(self, x): x = self.dropout(x) x = self.dense(x) x = torch.tanh(x) x = self.dropout(x) x = self.out_proj(x) return x @add_start_docstrings( """ LayoutLMv3 Model with a token classification head on top (a linear layer on top of the final hidden states) e.g. for sequence labeling (information extraction) tasks such as [FUNSD](https://guillaumejaume.github.io/FUNSD/), [SROIE](https://rrc.cvc.uab.es/?ch=13), [CORD](https://github.com/clovaai/cord) and [Kleister-NDA](https://github.com/applicaai/kleister-nda). """, LAYOUTLMV3_START_DOCSTRING, ) class LayoutLMv3ForTokenClassification(LayoutLMv3PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.layoutlmv3 = LayoutLMv3Model(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) if config.num_labels < 10: self.classifier = nn.Linear(config.hidden_size, config.num_labels) else: self.classifier = LayoutLMv3ClassificationHead(config, pool_feature=False) self.init_weights() @add_start_docstrings_to_model_forward( LAYOUTLMV3_DOWNSTREAM_INPUTS_DOCSTRING.format("batch_size, sequence_length") ) @replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, bbox: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, pixel_values: Optional[torch.LongTensor] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. Returns: Examples: ```python >>> from transformers import AutoProcessor, AutoModelForTokenClassification >>> from datasets import load_dataset >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) >>> model = AutoModelForTokenClassification.from_pretrained("microsoft/layoutlmv3-base", num_labels=7) >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train", trust_remote_code=True) >>> example = dataset[0] >>> image = example["image"] >>> words = example["tokens"] >>> boxes = example["bboxes"] >>> word_labels = example["ner_tags"] >>> encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="pt") >>> outputs = model(**encoding) >>> loss = outputs.loss >>> logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.layoutlmv3( input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, pixel_values=pixel_values, ) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # only take the text part of the output representations sequence_output = outputs[0][:, :seq_length] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ LayoutLMv3 Model with a span classification head on top for extractive question-answering tasks such as [DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the text part of the hidden-states output to compute `span start logits` and `span end logits`). """, LAYOUTLMV3_START_DOCSTRING, ) class LayoutLMv3ForQuestionAnswering(LayoutLMv3PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.layoutlmv3 = LayoutLMv3Model(config) self.qa_outputs = LayoutLMv3ClassificationHead(config, pool_feature=False) self.init_weights() @add_start_docstrings_to_model_forward( LAYOUTLMV3_DOWNSTREAM_INPUTS_DOCSTRING.format("batch_size, sequence_length") ) @replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, bbox: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.LongTensor] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. Returns: Examples: ```python >>> from transformers import AutoProcessor, AutoModelForQuestionAnswering >>> from datasets import load_dataset >>> import torch >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) >>> model = AutoModelForQuestionAnswering.from_pretrained("microsoft/layoutlmv3-base") >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train", trust_remote_code=True) >>> example = dataset[0] >>> image = example["image"] >>> question = "what's his name?" >>> words = example["tokens"] >>> boxes = example["bboxes"] >>> encoding = processor(image, question, words, boxes=boxes, return_tensors="pt") >>> start_positions = torch.tensor([1]) >>> end_positions = torch.tensor([3]) >>> outputs = model(**encoding, start_positions=start_positions, end_positions=end_positions) >>> loss = outputs.loss >>> start_scores = outputs.start_logits >>> end_scores = outputs.end_logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.layoutlmv3( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, bbox=bbox, pixel_values=pixel_values, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[1:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ LayoutLMv3 Model with a sequence classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for document image classification tasks such as the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset. """, LAYOUTLMV3_START_DOCSTRING, ) class LayoutLMv3ForSequenceClassification(LayoutLMv3PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.layoutlmv3 = LayoutLMv3Model(config) self.classifier = LayoutLMv3ClassificationHead(config, pool_feature=False) self.init_weights() @add_start_docstrings_to_model_forward( LAYOUTLMV3_DOWNSTREAM_INPUTS_DOCSTRING.format("batch_size, sequence_length") ) @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, bbox: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.LongTensor] = None, ) -> Union[Tuple, SequenceClassifierOutput]: """ Returns: Examples: ```python >>> from transformers import AutoProcessor, AutoModelForSequenceClassification >>> from datasets import load_dataset >>> import torch >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) >>> model = AutoModelForSequenceClassification.from_pretrained("microsoft/layoutlmv3-base") >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train", trust_remote_code=True) >>> example = dataset[0] >>> image = example["image"] >>> words = example["tokens"] >>> boxes = example["bboxes"] >>> encoding = processor(image, words, boxes=boxes, return_tensors="pt") >>> sequence_label = torch.tensor([1]) >>> outputs = model(**encoding, labels=sequence_label) >>> loss = outputs.loss >>> logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.layoutlmv3( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, bbox=bbox, pixel_values=pixel_values, ) sequence_output = outputs[0][:, 0, :] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) __all__ = [ "LayoutLMv3ForQuestionAnswering", "LayoutLMv3ForSequenceClassification", "LayoutLMv3ForTokenClassification", "LayoutLMv3Model", "LayoutLMv3PreTrainedModel", ]